Intro
Packages
library(tidyverse)
library(magrittr)
library(feather)
library(effects)
theme_set(theme_minimal(base_size = 16))
Data
Expanded model
orplot(expglm_df, slice_range=2:15, ref_level="19-24", prefix="age_grp", ylab="Age groups")
orplot(expglm_df, slice_range=16, ref_level="Female", prefix="sex", ylab="Sex")
orplot(expglm_df, slice_range=57:65, ref_level="Decile 10 - highest", prefix="loinc_decile", ylab="Income deciles")
orplot(expglm_df, slice_range=67:78, ref_level="Ontario", sorted=TRUE, prefix="province", ylab="Province")
orplot(expglm_df, slice_range=43:44, ref_level="Born in Canada, both parents born in Canada", prefix="generation", ylab="Migrant generation")
orplot(expglm_df, slice_range=45:56, ref_level="High school", sorted=TRUE, prefix="education", ylab="Education")
orplot(expglm_df, slice_range=79:82, ref_level="Married", sorted=TRUE, prefix="mar_stat", ylab="Marital status")
orplot(expglm_df, slice_range=83:84, ref_level="No kids", prefix="no_kids", ylab="No. of children")
orplot(expglm_df, slice_range=17, ref_level="No aboriginal ID", prefix="ab_id_dichot", ylab="Aboriginal ID")
orplot(expglm_df, slice_range=27:37, ref_level="Not a visible minority (except indigenous)", prefix="vis_minority", ylab="Visible minority (except indigenous)", sorted=TRUE)
orplot(expglm_df, slice_range=41:42, ref_level="Full-time work", prefix="employ_status", ylab="Employment status")
orplot(expglm_df, slice_range=38:40, ref_level="English", prefix="first_lang", ylab="First language")
orplot(expglm_df, slice_range=18:20, ref_level="No ADL difficulties", prefix="adl_difficulty", ylab="Difficulty in activities of daily living")
Model with interactions
orplot(intglm_df, slice_range=2:15, ref_level="19-24", prefix="age_grp", ylab="Age groups")
orplot(intglm_df, slice_range=16, ref_level="Female", prefix="sex", ylab="Sex")
orplot(intglm_df, slice_range=17, ref_level="No aboriginal ID", prefix="ab_id_dichot", ylab="Aboriginal ID")
orplot(intglm_df, slice_range=57:65, ref_level="Decile 10 - highest", prefix="loinc_decile", ylab="Income deciles")
orplot(intglm_df, slice_range=67:78, ref_level="Ontario", sorted=TRUE, prefix="province", ylab="Province")
orplot(intglm_df, slice_range=43:44, ref_level="Born in Canada, both parents born in Canada", prefix="generation", ylab="Migrant generation")
orplot(intglm_df, slice_range=45:56, ref_level="No formal education", sorted=TRUE, prefix="education", ylab="Education")
orplot(intglm_df, slice_range=79:80, ref_level="Married", sorted=TRUE, prefix="mar_stat", ylab="Marital status")
orplot(intglm_df, slice_range=83:84, ref_level="No kids", prefix="no_kids", ylab="No. of children")
orplot(intglm_df, slice_range=27:37, ref_level="Not a visible minority (except indigenous)", prefix="vis_minority", ylab="Visible minority (except indigenous)", sorted=TRUE)
orplot(intglm_df, slice_range=41:42, ref_level="Full-time work", prefix="employ_status", ylab="Employment status")
orplot(intglm_df, slice_range=38:40, ref_level="English", prefix="first_lang", ylab="First language")
orplot(intglm_df, slice_range=18:20, ref_level="None", prefix="adl_difficulty", ylab="Difficulty in activities of daily living")
Effect plots